Satellite remote sensing of isolated wetlands using object-oriented classification of Landsat-7 data

Frohn, Robert C.; Reif, Molly; Lane, Charles R.; Autrey, Bradley C.

doi:10.1672/08-194.1

Cited by 90 publications

(74 citation statements)

References 19 publications

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“…These included peer-reviewed journal articles, conference papers and chapters of book and PhD thesis publications. This review specifically focused on vegetated wetlands (i.e., colonized by aquatic macrophytes) of diverse types [36] such as fresh-and salt-water estuarine, riverine, lacustrine, palustrine and selected marine (mangrove) ecosystems (Table 1, [5][6][7][8]12,13,[15][16][17][18][22][23][24][25][26][27][28][29][30]32,34,) and excluded studies of solely deepwater and open-water aquatic ecosystems such as seagrass beds. The OBIA applications and main research objectives fell into several broad groups (with some papers applicable to more than one of these):…”

Section: Research Objectives and Focusmentioning

confidence: 99%

“…Wetland detection & delineation: studies detecting wetland presence and/or delineating their boundaries in landscapes with significant proportion of non-wetland natural and anthropogenic land cover [14][15][16]18,26,29,[38][39][40]55,63,64,73,74,[84][85][86][87]. (2).…”

Section: Research Objectives and Focusmentioning

confidence: 99%

“…First, heterogeneity in local pixel values, variable soil moisture and spectral similarities among cover types often constrain discrimination of classes and lead to infamous "salt-and-pepper" speckle in the mapping outputs [12][13][14]. Second, spatial resolution of commonly used image datasets (such as 30-m Landsat) is frequently insufficient to detect fine-scale wetland features and class boundaries, and spectral mixing of different classes at subpixel scales may reduce mapping accuracy [15][16][17]. Finally, pixel-based approaches may be limited by the lack of ecologically meaningful information on spatial context and class neighborhood relationships in the pixel grid structure [18], unless ancillary geospatial or field information is available to inform wetland delineation and classification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Object-Based Image Analysis in Wetland Research: A Review

2015

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Abstract:The applications of object-based image analysis (OBIA) in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA offers several important benefits to wetland analyses related to smoothing of the local noise, incorporating meaningful non-spectral features for class separation and accounting for landscape hierarchy of wetland ecosystem organization and structure. However, there has been little discussion on whether unique challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting. This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives. It summarizes the progress and scope of OBIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIA capacity in the future wetland studies. Growing demands for higher-accuracy wetland characterization at both regional and local scales together with advances in very high resolution remote sensing and novel tasks in wetland restoration monitoring will likely continue active exploration of the OBIA potential in these diverse and complex environments.

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Section: Research Objectives and Focusmentioning

confidence: 99%

Section: Research Objectives and Focusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Object-Based Image Analysis in Wetland Research: A Review

2015

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“…Field data were collected on 16,17,24,25, and 31 March 2015, as well as 1 and 6 April 2015 on two Nature Conservancy properties, one located east of Tuckahoe Creek and the other just west of the main stem of the Choptank River ( Figure 1). The field effort was designed to coincide with the Radarsat-2 and Worldview-3 satellite flyover dates.…”

Section: Field Data Collectionmentioning

confidence: 99%

Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA

et al. 2017

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Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality.

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“…Mapping studies by Lunetta and Balogh (1999), Berberoglu et al (2004), Klemas (2005), Phillips et al (2005), Frohn et al (2009), and Klemas (2011), have demonstrated the potential of applying remote sensing methods to wetland identification. None of these, however, were concerned with the wetland footprint as a direct foundation for water management decision-making based on modeled simulations of water quality.…”

Section: Applications Of Remote Sensing To Wetland and Land-water Intmentioning

confidence: 99%

Seasonally-managed wetland footprint delineation using Landsat ETM+ satellite imagery

Quinn

Epshtein

2014

Environmental Modelling & Software

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One major challenge in water resource management is the estimation of evapotranspiration losses from seasonally managed wetlands. Quantifying these losses is complicated by the dynamic nature of the wetlands' areal footprint during the periods of flood-up and drawdown. In this study we present a data-lean solution to this problem using an example application in the San Joaquin River Basin of California, USA. Through analysis of high-resolution (30 meter) Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery, we develop a metric for more fully capturing the extent of total flooded wetland area. The procedure is validated using year-long, continuously-logged field datasets at two separate wetlands within the study area. Based on this record, the proposed classification using a Landsat ETM+ Band 5 (mid-IR wavelength) to Band 2 (visible green wavelength) ratio improves estimates by 30-50% relative to previous attempts at wetland delineation. Requiring modest ancillary data, the results of our study provide a practical and efficient option for wetland management in data-sparse regions or un-gauged watersheds.

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Satellite remote sensing of isolated wetlands using object-oriented classification of Landsat-7 data

Cited by 90 publications

References 19 publications

Object-Based Image Analysis in Wetland Research: A Review

Object-Based Image Analysis in Wetland Research: A Review

Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA

Seasonally-managed wetland footprint delineation using Landsat ETM+ satellite imagery

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